Abstract: Disclosed are a point cloud encoding and decoding method and device based on a two-dimensional regularization plane projection. The encoding method includes: acquiring original point cloud data; performing two-dimensional regularization plane projection on the original point cloud data to obtain a two-dimensional projection plane structure; obtaining a plurality of pieces of two-dimensional image information according to the two-dimensional projection plane structure; and encoding the plurality of pieces of two-dimensional image information to obtain code stream information.

Abstract: Disclosed are a point cloud encoding and decoding method and device based on a two-dimensional regularization plane projection. The encoding method includes: acquiring original point cloud data; performing two-dimensional regularization plane projection on the original point cloud data to obtain a two-dimensional projection plane structure; obtaining a plurality of pieces of two-dimensional image information according to the two-dimensional projection plane structure; and encoding the plurality of pieces of two-dimensional image information to obtain code stream information.

Abstract: The implementation of the present disclosure provides a point cloud partitioning method and device, and a computer-readable storage medium, including: when performing stripe division along the longest side, adjusting an initial partitioning position or determining the stripe division length according to the size of a preset block, to obtain a stripe division position, wherein, the length of the first n?1 stripes along the longest side is an integer multiple of the side length of the preset block, n is the number of divided stripes, and n is a positive integer greater than or equal to 2.

Abstract: A method for coding includes: obtaining original point cloud data; creating an adaptive prediction list of the attribute information of the point cloud; selecting a prediction mode from the adaptive prediction list and predicting the attribute information of the point cloud, to obtain a predicted residual; and coding the prediction mode and the predicted residual, to obtain codestream information.

Abstract: The present application discloses a point cloud encoding/decoding method and apparatus based on two-dimensional regularized plane projection. The encoding method includes: acquiring raw point cloud data; performing two-dimensional regularized plane projection on the raw point cloud data to obtain a two-dimensional projection plane structure; obtaining one or more pieces of two-dimensional graphic information based on the two-dimensional projection plane structure; and encoding the one or more pieces of two-dimensional graphic information to obtain bit stream information. In the present application, a strong correlation representation of a point cloud on a two-dimensional projection plane structure is obtained using a two-dimensional regularized plane projection technology, which highlights spatial correlation of the point cloud.

Abstract: The implementation of the present disclosure provides a point cloud partitioning method and device, and a computer-readable storage medium, including: when performing stripe division along the longest side, adjusting an initial partitioning position or determining the stripe division length according to the size of a preset block, to obtain a stripe division position, wherein, the length of the first n?1 stripes along the longest side is an integer multiple of the side length of the preset block, n is the number of divided stripes, and n is a positive integer greater than or equal to 2.

Abstract: Disclosed are a point cloud encoding and decoding method and device based on a two-dimensional regularization plane projection. The encoding method includes: acquiring original point cloud data; performing two-dimensional regularization plane projection on the original point cloud data to obtain a two-dimensional projection plane structure; obtaining a plurality of pieces of two-dimensional image information according to the two-dimensional projection plane structure; and encoding the plurality of pieces of two-dimensional image information to obtain code stream information. According to the present disclosure, the strong correlation representation of a point cloud on the two-dimensional projection plane structure is obtained, so that the spatial correlation of the point cloud is better reflected, and the encoding efficiency of the point cloud is improved. Moreover, a placeholder information map is used for assisting in encoding the depth information map, so that the encoding efficiency is further improved.

Abstract: Point cloud coding methods, an encoder, and a decoder are provided. The method includes the following. Morton codes for a point cloud are obtained according to geometry information of the point cloud after geometry decoding is completed and the geometry information is reconstructed. A value of K is determined according to a distribution of the point cloud, where K is a positive integer, where K is a positive integer. The point cloud is determined as an input point cloud of an iteration. Morton codes for an input point cloud of an i-th iteration are right shifted by K bits according to a number of points in the input point cloud of the i-th iteration. An i-th level of detail (LOD) layer is formed with a point selected after the Morton codes are shifted. Color information is decoded according to the LOD layer.

Abstract: A point cloud model reconstruction method, an encoder, and a decoder are provided. The method includes the following. An i-th segmentation position of an i-th three-dimensional slice on a longest edge in a point cloud space is obtained, where 1?i?n?1 and n is a total number of three-dimensional slices to-be-processed. An i-th cube block set nearest to the i-th segmentation position in the i-th three-dimensional slice is determined based on the i-th segmentation position and a preset cube block length. An i-th vertex position with a cube block in the i-th cube block set is determined along the longest edge, where the i-th vertex position represents a position of a vertex on an edge which is away from a start position of the cube block by the preset cube block length. A point cloud model is reconstructed based on the i-th vertex position.

Abstract: Point cloud coding methods, an encoder, and a decoder are provided. The method includes the following. Morton codes for a point cloud are obtained according to geometry information of the point cloud after geometry decoding is completed and the geometry information is reconstructed. The point cloud is determined as an input point cloud of an iteration. Morton codes for an input point cloud of an i-th iteration are right shifted by K bits according to a number of points in the input point cloud of the i-th iteration, where K is a positive integer. A point is selected from points with a same value of Morton codes after right shifted by K bits. An i-th level of detail (LOD) layer is formed with the point selected. Color information is decoded according to the LOD layer.

Abstract: A nearest neighbour search method, an encoder, a decoder and a storage medium are provided. The method includes: acquiring a Morton code set of point cloud data to be searched; according to the Morton code set and a first distance threshold value, layering the point cloud data to obtain current-layer data; shifting, by a first preset number of bits, to the right a Morton code bit of prediction data corresponding to the current-layer data in the point cloud data, so as to obtain a corresponding first parent node set; according to a Morton code of the current-layer data, determining a neighbour area in the first parent node set, and determining, within the neighbour area, a nearest neighbour point set of the current-layer data and performing searching within a first preset search range corresponding to the search point to determine the nearest neighbour point set.

Abstract: The point cloud encoding/decoding method, an encoder, a decoder, and a storage medium are provided. The method comprises: determining a bounding box of a point cloud to be encoded/decoded; when performing octree division on the bounding box of said point cloud on the basis of a Morton code order, obtaining coordinate information of the current node; determining a first neighbor mode on the basis of the coordinate information; determining an entropy encoding parameter/entropy decoding parameter of the current node on the basis of the first neighbor mode; and performing entropy encoding/entropy decoding on occupation information of the current node on the basis of the entropy encoding parameter/entropy decoding parameter.

Abstract: Point cloud coding methods, an encoder, and a decoder are provided. The method includes the following. Morton codes for a point cloud are obtained according to geometry information of the point cloud after geometry decoding is completed and the geometry information is reconstructed. The point cloud is determined as an input point cloud of an iteration. Morton codes for an input point cloud of an i-th iteration are right shifted by K bits according to a number of points in the input point cloud of the i-th iteration, where K is a positive integer. A point is selected from points with a same value of Morton codes after right shifted by K bits. An i-th level of detail (LOD) layer is formed with the point selected. Color information is decoded according to the LOD layer.

Abstract: Disclosed are a partitioning method, an encoder, a decoder and a computer storage medium. The method includes: determining location information of a point of a point cloud to be partitioned; when i is less than or equal to M?1, determining right-shift number Ni of ith LOD layer in the point cloud, M representing a preset maximum quantity of layers for LOD partitioning; for the ith LOD layer, shifting location information of the point rightwards by Ni-digit, performing storing in a preset storage area based on right-shifted location information; determining location information of a parent point corresponding to a current point in the ith LOD layer; according to determined location information of the parent point, searching the preset storage area for a neighbor point of the parent point; partitioning the current point into an (i+1)th LOD layer, or the neighbor point into the ith LOD layer.

Abstract: Implementations of this application provide point cloud partition methods, an encoder, and a decoder. The method includes the following. Morton codes for a point cloud are obtained according to geometry information of the point cloud. The point cloud is determined as an input point cloud of an iteration. Morton codes for an input point cloud of an i-th iteration are right shifted by K bits according to a number of points in the input point cloud of the i-th iteration, where K is a positive integer. A point is selected from points with a same value of Morton codes after right shifted by K bits. An i-th level of detail (LOD) layer is formed with the point selected.

Abstract: Described is a nearest neighbor search method. The method is applicable in a device and comprises: acquiring a Morton code set of point cloud data to be searched; stratifying the point cloud data on the basis of the Morton code set and of a first distance threshold to produce current stratum data; shifting to the right by a first preset digit Morton codes of prediction data other than the stratified data in the point cloud data to produce a corresponding first parent node set; determining, on the basis of Morton codes of the current stratum data, a neighbor area satisfying a criterion in the first parent node set; and determining a nearest neighboring point set of the current stratum data in the neighboring area. Also provided in an exemplary embodiment of the present application are a device and a computer storage medium.

Abstract: Provided are a point cloud processing method, an encoder and a decoder.

Abstract: Implementations of this application provide point cloud partition methods, an encoder, and a decoder. The method includes the following. Morton codes for a point cloud are obtained according to geometry information of the point cloud. The point cloud is determined as an input point cloud of an iteration. Morton codes for an input point cloud of an i-th iteration are right shifted by K bits according to a number of points in the input point cloud of the i-th iteration, where K is a positive integer. A point is selected from points with a same value of Morton codes after right shifted by K bits. An i-th level of detail (LOD) layer is formed with the point selected.

Abstract: Provided in the embodiments of the present application are a transform method, inverse transform method, encoder, decoder and storage medium.

Abstract: A point cloud model reconstruction method, an encoder, and a decoder are provided. The method includes the following. An i-th segmentation position of an i-th three-dimensional slice on a longest edge in a point cloud space is obtained, where 1?i?n?1 and n is a total number of three-dimensional slices to-be-processed. An i-th cube block set nearest to the i-th segmentation position in the i-th three-dimensional slice is determined based on the i-th segmentation position and a preset cube block length. An i-th vertex position with a cube block in the i-th cube block set is determined along the longest edge, where the i-th vertex position represents a position of a vertex on an edge which is away from a start position of the cube block by the preset cube block length. A point cloud model is reconstructed based on the i-th vertex position.